Image enhancement algorithms that use a Bayesian based, Richardson and Lucy iteration approach for under-sampled imaging systems have been developed. These image enhancement algorithms may improve the radiometric accuracy of objects in the image by attempting to bring the objects back to their true radiometric values. These objects in the image are, typically, under sampled by a factor of 2.4, using a Rayleigh sampled imaging system.
There are fundamental problems with the current algorithms. First, these algorithms enhance objects of different sizes at different rates within the image. This results in some objects in the image not being enhanced enough, and some objects in the image being over enhanced, such that the radiometric error of an object may actually increase.
Second, these algorithms use a number of iterations or computer runs as a parameter to determine how much the image is enhanced. Without a “truth” image, is it very difficult to determine how much iteration to give an image, in order to minimize radiometric errors of the objects within the image. If the number of iterations is too small, the objects within the image may be under enhanced. If the number of iterations is too large, however, the objects within the image may be over enhanced, thereby increasing the errors.
The present invention addresses these problems.